At initialisation (step 0 in Figure REF ), our SSE model is pretrained with the NTXEnt loss
on batches of positive pairs obtained by data augmentation. First, we apply Voice Activity Detection (rVAD [1]}) to segment LibriSpeech into continuous Vocal Activity (VA) segments without any silence. Next, we use data augmentation on these speech sequences.
Preliminary experiments showed that of the various augmentations (pitch, reverberation, additive noise, masking, etc), only time stretch had a large and systematic effect. This makes sense as variations in speech rate is a cause of a major change in the acoustic representation of a given speech sequence: its length. We therefore use time stretch as the sole data augmentation method.
